Calipsocean meeting
05/03/2024
Can we improve the representation of zooplankton diversity in ESM?
As in Wang et al., 2023.
Yearly climatologies from GLODAPv2.
temperature
silicate
phosphate
oxygen
NPP
alkalinity
DIC
DOC
UVP5 dataset: 2876 profiles
Function diversity metrics (Magneville et al. 2022)
→ morphological diversity metrics (Beck et al., 2023)
morphological richness
morphological divergence
morphological evenness
…
by taxa-region matches?
Benedetti et al., 2023 (copepods only)
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439 data points
Learning* VS test set, stratified by POC.
TODOs
Account for spatial autocorrelation (spatial CV)
Get more robust estimates of R² (nested CV)
Response variable:
uni- or multivariate
~normally distributed → log(POC)
Flexibility for predictors, handles interactions.
Complex & non-linear relationships.
Easy interpretation & implementation.
POC ~ temperature + silicate + phosphate + oxygen + NPP + alkalinity + DIC
R² = 89.1%
Good prediction!
POC ~ all plankton metrics
R² = 57.1%
OK prediction!
Best predictors:
ta. evenness
ta. diversity
mo. richness
POC ~ ta_ric_3 + ta_mast + mo_ric
R² = 47.4%
OK prediction!
POC ~ ta_ric_3 + ta_mast + mo_ric
POC response to plankton descriptors.
TODO: Merge both descriptors of taxonomic richness into one.
ta_ric_3 + ta_mast + mo_ric ~ temperature + silicate + phosphate + oxygen + NPP + alkalinity + DIC
Mult. R² = 37.8%
OK prediction!